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All Journal Journal of Intelligent Decision Support System (IDSS) Jurnal Minfo Polgan (JMP)
Simanjuntak, Nurmala Dewi
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Computer Science & IT Library & Information Science Mathematics Social Sciences

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Penerapan Transfer Learning untuk Klasifikasi Citra Bunga Berbasis Convolutional Neural Network Simangunsong, Juanto; Simanjuntak, Nurmala Dewi; Matondang, Aprima Anugerah
Jurnal Minfo Polgan Vol. 14 No. 1 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i1.14980

Abstract

Klasifikasi citra bunga merupakan salah satu tantangan dalam pengolahan citra digital yang memerlukan ketelitian tinggi dalam mengenali pola visual yang kompleks. Penelitian ini bertujuan untuk menerapkan metode transfer learning berbasis arsitektur Convolutional Neural Network (CNN) dalam mengklasifikasikan citra bunga dari dataset Oxford 102 Flower. Model pretrained yang digunakan meliputi VGG16, ResNet50, dan MobileNetV2, dengan penyesuaian pada layer output untuk mendukung klasifikasi 102 kelas bunga. Data citra diproses melalui teknik augmentasi dan normalisasi, kemudian dibagi menjadi data latih, validasi, dan uji. Hasil pelatihan menunjukkan bahwa model mampu mencapai akurasi validasi di atas 90%, dengan nilai loss yang menurun secara konsisten, tanpa indikasi overfitting. ResNet50 menunjukkan performa terbaik dalam hal keseimbangan antara akurasi dan stabilitas loss. Kesimpulannya, transfer learning terbukti efektif dan efisien dalam menyelesaikan tugas klasifikasi citra bunga, serta memiliki potensi besar untuk diimplementasikan dalam aplikasi identifikasi tanaman otomatis.
Mental disorder classification with exploratory data analysis (EDA) Simangunsong, Juanto; Simanjuntak, Mutiara S; Simanjuntak, Nurmala Dewi
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 3 (2024): Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i3.252

Abstract

Classification of mental disorders is the process of grouping mental disorders into categories based on their symptoms, causes and consequences.  EDA is a data analysis strategy that emphasizes open-mindedness, creativity and diverse perspectives. EDA aims to explore as much data as possible, without imposing previous assumptions or models, until a coherent, coherent story emerges. EDA can help generate new hypotheses, identify patterns and outliers, and uncover underlying structures and relationships in data. This paper shows how EDA can be used to analyze and understand mental disorders data from a variety of sources and perspectives. We used EDA methods to explore the characteristics, prevalence, and distribution of mental disorders, as well as the relationships and interactions between mental disorders and other variables. We also compared EDA results with mental disorder classification systems such as the Diagnostic and Statistical Manual of Mental Disorders (DSM). We show that EDA can provide a more comprehensive and nuanced understanding of mental disorder data, as well as highlight the challenges and limitations of mental disorder classification. We hope this paper will illustrate the potential and benefits of EDA for mental disorders research and practice
Co-Authors Baehaqi Mutiara S. Simanjuntak Simangunsong, Juanto